In the field of single prediction, these global influence methods have served as a basis to [1]}, using the same randomization technique. Given a single instance, the importance of each attribute is obtained by looking at the evolution of the prediction performance of the model on the instance when all of its values are swapped with other values of the dataset except the value of the attribute being studied. The more the prediction varies with the values swapping, the less the fixed attribute is important for the prediction. This method has the interest of relying on the same principle as the global technique which is largely recognized, but the main caveat is its computational cost needed for explaining the prediction of only one instance, as a large number of new predictions has to be generated for each single instance prediction to explain. Another caveat is this prediction explanation is realized from the point of view of model performance. Meaning that their metric shows which feature improves the performance of the model, rather than which feature the model consider as important for its prediction. If this line of reasoning is interesting for the model explanation field, it does not correspond to our scope, as we are aiming to help users understand how a model works, and not how to improve it.
